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Hyperspectral Image Compression Using JPEG2000 and Principal Component Analysis

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2 Author(s)
Qian Du ; Dept. of Electr. & Comput. Eng., Mississippi State Univ., MS ; James E. Fowler

Principal component analysis (PCA) is deployed in JPEG2000 to provide spectral decorrelation as well as spectral dimensionality reduction. The proposed scheme is evaluated in terms of rate-distortion performance as well as in terms of information preservation in an anomaly-detection task. Additionally, the proposed scheme is compared to the common approach of JPEG2000 coupled with a wavelet transform for spectral decorrelation. Experimental results reveal that, not only does the proposed PCA-based coder yield rate-distortion and information-preservation performance superior to that of the wavelet-based coder, the best PCA performance occurs when a reduced number of PCs are retained and coded. A linear model to estimate the optimal number of PCs to use in such dimensionality reduction is proposed

Published in:

IEEE Geoscience and Remote Sensing Letters  (Volume:4 ,  Issue: 2 )